Discovering intrinsic multi-compartment pharmacometric models using Physics Informed Neural Networks

TL;DR

This paper introduces PKINNs, a data-driven neural network approach that efficiently discovers interpretable multi-compartment pharmacometric models, potentially transforming drug discovery processes by reducing reliance on manual derivation.

Contribution

The paper presents PKINNs, a novel neural network framework that automatically uncovers intrinsic pharmacometric structures from data, combining efficiency with interpretability.

Findings

Successfully models multi-compartment pharmacokinetics

Provides reliable derivative forecasting

Enables symbolic regression for interpretability

Abstract

Pharmacometric models are pivotal across drug discovery and development, playing a decisive role in determining the progression of candidate molecules. However, the derivation of mathematical equations governing the system is a labor-intensive trial-and-error process, often constrained by tight timelines. In this study, we introduce PKINNs, a novel purely data-driven pharmacokinetic-informed neural network model. PKINNs efficiently discovers and models intrinsic multi-compartment-based pharmacometric structures, reliably forecasting their derivatives. The resulting models are both interpretable and explainable through Symbolic Regression methods. Our computational framework demonstrates the potential for closed-form model discovery in pharmacometric applications, addressing the labor-intensive nature of traditional model derivation. With the increasing availability of large datasets, this framework holds the potential to significantly enhance model-informed drug discovery.